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【完成】第二十三篇 The right mix between statistical engineering applied statist

本帖最后由 小编H 于 2011-8-8 15:21 编辑 _

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本文由http://www.6sq.net/space-uid-412863.html翻译 天外流星2008校稿


The right mix between statistical engineering applied statistics统计工程和应用统计的良好结合

by Ronald D. Snee and Roger W. Hoerl
RonaldD.Snee和RogerW.Hoer著

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内容摘自网上

Since we proposed the idea and theory behind statistical engineering, we’re often asked: "Is it just another term for traditional applied statistics?" That’s a legitimate question.
自从我们提出统计工程背后的构思和理论,我们经常被问及:这仅仅是传统应用统计学的另一个术语吗?这是一个值得探讨的问题。

To answer that, the relationship between statistical engineering and applied statistics—as it has been traditionally practiced—must be addressed. We believe statistical engineering can make greater use of strategic thinking and systems thinking. In fact, a blend of statistical engineering and traditional applied statistics is what’s needed.
为了回答这个问题,必须找到传统结合的实践证明来说明统计工程和应用统计之间的关系,我们认为,统计工程最大化利用了战略思维和系统思维。实际上,统计工程和传统的应用统计的结合才正是我们所需要的。

**New way of thinking一种新的思维方法

Statistical engineering is defined as "the study of how to best utilize statistical concepts, methods and tools, and integrate them with information technology and other relevant sciences to generate improved results."1 This definition provides a new way of thinking about the use of statistical thinking and methods. It is consistent with dictionary definitions of engineering, which emphasize the study of using existing science for the betterment of humankind.统计工程定义为“研究怎样最好的利用统计概念、统计方法及统计工具,把它和信息技术以及其他相关的学科整合在一起来达到一个改进的效果”。这个定义提供了一种新的使用统计思维和方法的思考方式。它和词典上的定义是一致的,强调了这种研究使用现有科学来更好的为人们服务**

In general, the statistics profession has primarily focused on advancing statistical science—the development and application of new methods—while not recognizing that statistical engineering is the "other side of the coin" that could enable statistics to have greater societal impact.2-8
一般来说,统计专业最开始集中在先进统计学科-即新方法的建立和应用,当时没有意识到统计工程的另一面可以使统计有很大的社会效应。

This sentiment became obvious during a presentation in January 2010 by Susan Hockfield, president of the Massachusetts Institute of Technology.

2010年1月哈克.菲尔在苏珊-马萨诸塞州技术委员会上做了一个演讲,让这个观点变得鲜明了。


To paraphrase, Hockfield explained that about the dawn of the 20th century, physicists discovered the basic building blocks of the universe—a parts list. Engineers said: "We can build something from this list." They produced the electronics revolution and, subsequently, the computer revolution.
为了阐明这个观点,哈克.菲尔解释道,20世纪初,物理学家发现一张构成宇宙的最基本的结构表,他说道:我们可以从这个表中发现一些东西。因而他们发起了电气革命,以及随后的电脑革命。

More recently, biologists have discovered and mapped the basic parts list of life—the human genome. Engineers have said: "We can build something from this list," and are producing a revolution in personalized medicine.
最近,生物学家发现并绘制了生命最基本的组成单元-人类基因组,他们说:我们可以从中发现点什么。因此发起了个性化的医药革命。

Relating this to statisticians, while there seems to be great emphasis within the profession on advancing the parts list of statistical methods, there is considerably less emphasis on building something of importance to society from this parts list.
把这些和统计联系起来,统计分支中的方法能能推动重要行业的进步,但社会较少重视统计分支机构的建立


For example, we speak of building overall approaches to problem solving or process improvement that involve multiple methods, such as Six Sigma, which we suggest as a positive counter-example.
例如,我们说建立系统的过程改进方法解决问题,会涉及到多样的方法,比如我们经常建议把六西格玛作为解决问题的正确例子


Statistics as a system统计系统


Figure 1 shows how statistical engineering, traditional applied statistics and statistical theory fit together.9 Consider statistical thinking as the strategic aspect of the discipline, providing a philosophy of thinking about statistics and its application. Statistical methods and tools are, of course, critical and where the "rubber hits the road" in terms of delivering value to society.

图一说明了统计工程、传统统计应用以及统计理论之间的联系与作用,把统计思想作为原则性战略的一个方面给思考思想和其应用提供了基本理论。统计方法和工具无疑很重要,向社会传递很重要的价值




Figure1

**




Statistical engineering can be viewed as the tactical element that provides overall approaches to attack big, unsolved problems that are consistent with the principles of statistical thinking. Statistical engineering links methods and tools with philosophy, and guides the use of the tools.
统计工程可以被视作为提供全面的、解决所有的遗留问题的战略元素,它和统计中思想原则是一致的,统计工程把方法和工具同理论联系起来,并指导工具的使用。

We noted Six Sigma as an example of statistical engineering as we have defined it. The philosophy of Six Sigma is based on statistical thinking: At a high level, it is used to improve processes, typically by reducing variation, thereby improving internal costs and customer satisfaction. The define, measure, analyze, improve and control method provides the tactics for integrating and using specific tools to implement the philosophy.
我们把六西格玛作为我们统计工程的一个例子,如我们定义的一样。六西格玛的哲学是建立在统计思想上,运用到一个高水准,它可以用来提高过程,典型的可以减少缺陷,因此降低了成本,提高了客户满意率。六西格码使用定义,测量,分析,改进和控制的方法来整合和使用工具

Taking on tradition传统的使用


Statistical engineering and traditional applied statistics are closely related concepts and shouldn’t be viewed as competing approaches. There are important, unique aspects of both, but we fear that greater emphasis on applied statistics will not, in itself, develop the field of statistical engineering to the degree needed.
统计工程和传统的应用统计与理念紧密相关,而不能被看做多个个体。很重要也很唯一的一个方面,我们担心不花费很多功夫在应用统计上就难达到需要的拓宽统计工程领域


There are many applications of applied statistics that involve presenting a real problem, questioning to clarify the problem, determining the appropriate methods to be used, and competently applying design or analysis tools, including checking assumptions, analysis of residuals and clear communication to nonstatistical clients.
有很多统计应用的实例,像描述一个真实存在的讨论,提问和解决问题,决定使用适当的方法,以及适当的应用设计或分析方法,包括验证假设问题和分析随机现象和非统计客户之间的联系。


In other cases, there is no known solution to a problem, nor is the development of a new statistical technique the right approach. The problem might be too big and complex for any one technique.
另一种情况,一个问题没有一个可行的解决方案,也没有找到一个正确的新统计技术的入口,这对于任何一项技术来说都是大而复杂的难题。


Rather, there is a need to create something new from the existing parts list of tools—a totally new approach, typically one that integrates several statistical and nonstatistical tools in a novel way. "Experience in Applied Statistics and Statistical Engineering" provides a personal example of this thinking.
然而,有必要从现有的列表工具中创造出一些新的东西-一个完全不同的新方法,典型的一个例子就是用一种新颖的方法整合了几个统计和非统计的工具。从应用统计和统计工程中学到的经验给这种思考提供了一个很好的个例。

**
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Experience in Applied Statistics and Statistical Engineering应用统计和统计工程的来历


In 1981-1982, Roger Hoerl worked as a summer intern in the DuPont Applied Statistics Group. This is where he was introduced to Ron Snee, who was a leader in this group.
1981-1982,Roger.Hoerl在DuPontAppliedStatisticsGroup当暑期实习生,也就是在那时,他被引荐给当时团队中的组长RonSnee。


By all accounts, Hoerl demonstrated good applied statistics by properly applying statistical techniques as needed to address design and analysis problems presented to him. His customers and supervisors were satisfied. Hoerl performed no statistical engineering. He did not develop anything new from the existing parts list of statistical tools.
据记载,Hoerl展示了在处理编码设计和现有存在问题的时候,很好的使用了应用统计,他的客户和领导都很满意,但Hoerl没有运用统计工程,也没有在现有的统计工具中有新发现。



Eventually, Hoerl moved on to Hercules Inc., a chemical manufacturing company based in Wilmington, DE, after graduate school. There again, he demonstrated good applied statistics. And again, he performed no statistical engineering.
最后,Hoerl毕业之后来到了HerculesInc,位于威尔明顿的一个化学品生产公司,又一次,他很好的展示了应用统计,又一次,没有运用统计工程。


When Hoerl left Hercules to join Scott Paper Co. in 1987, his boss told him: "We did not hire you to analyze data, we hired you to figure out how to effectively deploy statistical process control across the company." Hoerl quickly realized that he had received no training to do this, nor could he find any textbooks that provided overall strategies for corporate-level deployment. He found many books on the techniques of statistical process control (SPC), but virtually nothing about how these techniques should be best integrated, deployed and used for maximum benefit, certainly not in a multinational corporation.
1987年Hoerl离开Hercules加入ScottPaperCo.,他的老板告诉他,我们不是雇佣你来分析数据,我们雇佣你来是为了解决怎样在全公司有效的利用统计过程控制,Hoerl很快认识到,没有受过任何培训,也找不到任何可以提供合作层面的可利用的整体策略的书籍来。他找了很多关于统计过程控制技术的书籍,但是很明显,没有找到关于怎样很好的整合这些技术的内容,以及在国际公司之间怎样利用和使用才达到最大效益。

Understanding statistical thinking and SPC methods, Hoerl realized that there needed to be a link to provide more guidance to deployment. Being a good applied statistician was not sufficient to ensure success in this new challenge.
成为一个好的应用统计家是不够的,Hoerl意识到需要利用指导并进行连接来理解了统计思想和统计过程控制方法,保证在新一轮挑战中成功。


_—R.D.S and R.W.H._
Returning to Figure 1, statistical engineering is a horizontal slice of this model, which integrates statistical theory with statistical practice. Statistical engineering needs to be based on a solid theoretical foundation to help determine what works, what doesn’t work and why. It then needs to provide overall approaches or tactics for how the theory can be used for maximum impact. In this sense, it will and has directly led to more impactful statistical applications.
回到图1,统计工程在模型中是横向部分,它整合了统计理论和统计实践,统计工程需要建立在一个坚实的理论基础之上来帮助分辨什么是有用的,什么是无用的以及为什么它需要提供一个怎样利用这些理论来获得最大的影响的全局的方法或战术。在这种程度上,它也会直接导向了有影响力的统计应用。


Table 1 elaborates on the uniqueness of statistical engineering relative to traditional applied statistics. Traditionally, applied statistics includes applying individual tools to relatively well-defined technical problems.
表1阐明了相关的统计工程和传统应用统计唯一性,传统来说,应用统计包括应用个别工具到已完全定义好的技术问题上。

Table1表1



传统的应用统计和统计工程问题的度量


问题度量
传统应用统计
统计工程

对组织很重要
低-中


有影响-财务、过程表现、客户、社会和环境
低-中


涉及相关的几个部门、小组或功用
少数几个
几个

复杂-技术上的、有争议的



信息来源
少数几个
很多

涉及工具的多少
少数几个
很多

用途
一些
必须

需求的可持续性
可能需要
必须




In many situations, however, problems are complex and not well defined. There are significant political and social challenges, in addition to the technical challenges. Data may come from multiple sources that are inconsistent or even conflicting with one another. There is no single correct statistical method that can address the totality of the problem.
在很多情况下,然而,问题复杂且未完全定义好,除了技术上的挑战还存在很多争议和社会挑战。数据来源于一些不一致甚至相冲突的途径。可以解释全部问题的正确单一的统计方法是不存在。

Rather, a novel approach to solution needs to be crafted using various methods in the statistical tool kit, as well as methods from other disciplines such as computer science or organizational effectiveness. We do not see such examples included in statistics textbooks, nor are they seriously discussed at statistical conferences.
在一定程度上,一个新颖的解决方案需要精心制作,在统计的配套工具中使用不同的方法以及其他学科像计算机科学或组织效应中方法,在统计的教科书中我们没有看到很多例子,也没有看到他们在统计会议认真中讨论。



Deepen your understanding 加深你的理解

The reason many consider traditional applied statistics more art than science becomes clearer when you consider what it takes to have an effective system:
当你琢磨拥有一个高效的系统需要花费些什么,把传统应用统计看做艺术而不是科学这样原因就变得很清楚。





Strategy: This is where we are going.
策略:这是我们最终的目的



Tactics: These are the roadmaps and principles to guide us.
战术:这是引领我们的地图和原则



Operations: This is how we will do the work needed to accomplish our objective.
操作:我们怎么来做来完成我们目的的工作 Without all three components, the resulting system is less effective. Up until the late 1980s and early 1990s, traditional applied statistics focused only on the operational component of the system. The strategic and tactical components were developed informally and specific to each problem. As such, it was too often an art form learned from experience with no supporting body of knowledge (BoK) or theory.
没有这三个元素,这个系统是没有效率的,直到20世纪80年代晚期和90年代早期,传统的应用统计聚焦在系统的操作元素,策略和战术元素正式形成以及细化到每一个问题。

In the late 1980s and early 1990s, the strategic piece of the system started to develop in the form of statistical thinking with its critical elements of process, variation and data.10 This provided a strategy and vision to guide us.
在20世纪80年代末和90年代初期,系统战略片段开始发展,以统计思维的形式以及重要元素过程、变差和数据。这提供了一个指导我们的战略和远景。

First, understand the process that generated the data and the context for the problem being investigated. Next, identify the sources of variation to understand the process, with the reduction of unwanted variation as the overriding goal.
首先,了解产生数据的过程和所调查问题的背景,其次,为理解过程识别缺陷来源,减少不必要的缺陷把它作为压倒性的目标。

The limiting aspect of working with only strategic and operational components comes into focus when you encounter large, unstructured complex problems. The goals and objectives are usually clear (strategy), and you have the tools and methods available (operations). But how do you:
与战略和业务条件合作的限制因素在你遇到大型的、非结构化的复杂问题时就会成为焦点。目的和目标是明确的,你有可用的方法和工具,但是你如何:






Provide structure to the problem so it can be effectively addressed?
提供问题结构以便有效的解决问题



Create a strategy for how the problem will be addressed? What parts of the problem will be addressed in what order? What is the game plan for attacking each one?
创建一个怎样解决问题的策略,问题的哪一部分以什么样的顺序解决,解决每一个步骤的计划是什么? Imagine you’re serving in a war. You’re ordered to take a certain objective, perhaps a city or island. You have enough well-trained troops, air support, ammunition, material, vehicles and tanks. How will you weave these components into an effective battle plan?
假如你在打仗,你被分配带一个某种任务,也许是一座城或一个岛。你有训练有素的部队,空军支援,弹药、物资和车辆坦克,你如何利用这些元素使之成为一个有效的作战计划?

With little or no formal plan (tactics), you charge into the battle. You fight hard, and heroics abound. You stick together, and after some time and great loss of personnel and materials, you prevail and take the objective.
或许有或许根本没有正式的计划(战术),你冲入战斗中,你苦战,身边英雄比比皆是,你一直挺着,经过一段时间,你的人员和物资损失巨大,你占了上风最终成功的占领目标。

You did so well that you get the chance to do it again. You take the same approach, but you have little systematic evaluation of what you did in the first battle—only faint memories. This time, you lose at a great cost. Is this the way to fight a war?
你做得如此好以致得到再做一次的机会。你采用同样的方法,但是你对第一次战斗没有系统的评估只有一些零碎的记忆。这次,你损失惨重,这是打仗的方法吗?

You should have used the available theory to guide you in the construction and implementation of the tactical plan. You should have implemented the plan and evaluated the results to better develop and execute the plan for the next time and revise the theory as needed.
你应该用可行的理论指导你的行为和战术去实施,你本该在实施了计划之后评价结果以便下次更好的发展和执行计划及修订所需要的理论。

In traditional applied statistics, there must be a better way to develop tactical plans and methods to connect strategy and operational tools so you increase the effectiveness of your work and continually improve your approaches. Statistical engineering provides this tactical component. The system is thus complete, containing all three components.
在传统的应用统计,结合策略和实际工具以及方法来制定战略计划是比较好的方法,这样你可以提升你的工作效率,持续改进你的方法。统计工程提供了战略组成部分。包涵了这三个元素系统才是完整的。


Strategic and systems thinking战略和系统思路

As problems being addressed become larger and more complex, the need to use strategic thinking and systems thinking becomes greater. Strategic thinking is needed to properly define the goals and objectives of the project and decide how the project will be conducted. Systems thinking is needed to understand how to:
要解决的问题变得越来越大也越来越复杂,使用策略思路和系统思想变得越来越迫切。运用统计思路很好的定义目的和项目目标,决定怎样开展有效的项目。运用统计思想来理解:





Fit the processes, people and functions together.
怎样把过程、人员和功能结合在一起



Improve the processes so that the goals and objectives are attained.

怎样改进过程以便达到目标和实现目的




Fit the tools together to create the desired solution.
结合工具来创造可行的方法 In general, the statistics and quality professions have not given adequate attention to each of these items. The term "systems thinking" is paid lip service—sometimes mentioned but rarely used. Strategy is given even less attention.
一般来说,统计和质量行业还没完全理解它中间的每一个条款,术语“系统思路”被冠以口惠而实不至,有时提一提但是很少用到,策略甚至得到了更少的关注。

This isn’t surprising. The quality profession has been focused on operational work—the creation and use of tools and methods—with little attention given to strategic and tactical components. This operational view and outlook has served the profession well in the past. But a number of important problems have been overlooked or poorly solved, hurting organizations and reflecting poorly on those involved, as well as the overall profession.
这一点都不奇怪,质量行业一直聚焦于操作工作-创造性和方法、工具的使用-很少关注策略或战术元素。在过去一直为这个行业服务着的是这种操作画面和展望,但是一些重要的问题却被忽视了或是简单应付了,不利于相关组织以及整个行业。

An exclusive focus on the operational view can put the profession at a disadvantage in today’s environment of large, unstructured and complex problems that need to be solved with competition from other professionals who want to provide the needed solutions. Strategic and systems thinking can no longer be brushed to the side. It must be addressed today.
专注于操作层面可能使这个行业处在了今天亟待解决的大型化、非结构化、复杂的问题环境中的不利位置,以及来自提供所需解决方案的专业人士竞争。战略和系统思想再也不能丢到一边,它必须在今天解决。


Training and BoK培训和知识主体

Many say applied statisticians are already solving large, unstructured and complex problems. This is happening to some extent, but questions remain:
很多人说,统计工程已经解决了一些大型、非结构化和复杂的问题,一定程度上确实是这样,但是问题是:





What theory is used to guide the attack on such problems?
什么理论用来指导解决这样的问题?



What textbooks, chapters or journal articles are used to develop approaches?
什么教科书、章节或期刊用来制定方法?




How are those entering the professions that attack such problems being trained?
~如何进入那些接受培训处理问题的行业?



Are the approaches indeed the best ones to use and pass along?
这些方法确实是最好使用和可以传递的方法吗? Usually, people say they rely on their experience, and they mentor those new to the field using past experiences to guide them. Such an approach is inefficient in time and resources, fails to codify and enhance what has been learned, and is difficult to learn and pass on.
通常,人们所说他们依靠经验,他们用过去经验去指导这些新人。这样的方法是没有效果的,不能很好的整理和夯实所学到的,而且也很难学和传递。

What’s needed is a BoK with a solid theoretical basis that is continuously refined and improved. Statistical engineering provides a theory to guide the analyst and, over time, develops better strategies, methods and approaches, resulting in a BoK that advances the field and helps others learn the field.
真正需要的是一个坚实的理论基础的知识主体,可以持续精炼和完善。统计工程给与指导分析和制定策略提供了方案理论,并提升领域和帮助他人学习。


Blend as necessary必要的协调

Some have incorrectly suggested we want to abandon traditional applied statistics for statistical engineering. As an integral part of statistical practice, applied statistics will always be needed. Not all problems have the breadth and scope of those requiring a statistical engineering approach. Also, statistical engineering becomes impotent if you do not have solid statistical methods and people who can apply them as part of the game plan.
有些人错误的建议为了统计工程我们需要抛弃传统的应用统计。作为统计整合的一部分,应用统计不可少,不是所有的问题都在统计工程方法的宽度和范围。同样的,如果你没有坚实的统计方法基础和能很好的运用它的团队成员,统计工程有时也变得无力。

A blend of traditional applied statistics and statistical engineering is appropriate. What the exact mix should be depends on the issue, organization and individuals involved.
传统的统计应用和统计工程的协调是很合宜的,真正应用取决于事项,组织和相关人员。

The opportunity to work on issues that require statistical engineering often starts with a solution to a problem using the traditional applied statistics approach. Good work provides the license to take on more important problems—those most likely needing statistical engineering to solve. It seems prudent to work toward statistical engineering making up as much of one’s work program as possible.
传统的应用统计方法需要统计工程的方法来解决方案。好的方案提供了承担更重要问题的通行证-很多都需要统计工程来解决。看来,尽可能把它当做工作项目,谨慎认为只是统计工程的一部分


Increasing the effect递增的效应

Many experienced statisticians have been crafting novel approaches from the parts list of tools for a long time. We have neither invented nor discovered this phenomenon. But there has not been sufficient theory to guide practitioners to apply statistical engineering to these complex challenges, nor has an appropriate BoK been developed via books, journal articles or conferences.
很多资深的统计员很长时间以来从已有的工具列表中精心制作出一套新颖的方法。我们没有创造和发现这个现象,没有足够的实践理论指导来把统计工程应用到这些复杂的难题中,也没有从书本,期刊或研讨会中发现合适的理论知识

Traditional applied statistics is alive and well; however, it is not sufficient to address big, complex and unsolved problems. Higher-level approaches founded on statistical thinking concepts are required to craft novel solutions. Remember, one size does not fit all, and each problem must be addressed on its own merits—although a well-developed theory and literature on statistical engineering will help practitioners significantly.
传统应用统计应用很强,但是在应付大而复杂的难解决的问题时显得无力,在统计思维概念中发现的高水平的方法用来制造新颖的方法。尽管一个发展齐全的统计工程理论和著作对实践者有很大的帮助。但请记住,一个案例并不代表了所有的,每一个被解决了的问题都有它自己特点,


Those who use statistical engineering will make a greater positive impact on their organizations. The statistical profession, too, will benefit. Attacking the associated problems that are more important to the organization can help advance the reputation of the individuals involved.
使用统计工程的人将对他们的组织产生积极的影响,统计行业当然也会受益. 相关人员极力解决对组织很重要的相关问题可以提升其知名度。


**Ronald D. Snee is president of Snee Associates LLC in Newark, DE. He has a doctorat

Ronald D. Snee is president of Snee Associates LLC in Newark, DE. He has a doctorate in applied and mathematical statistics from Rutgers University in New Brunswick, NJ. Snee has received the ASQ Shewhart and Grant Medals. He is an ASQ fellow and an Academician in the International Academy for Quality.
RonaldD.Snee是纽瓦克SneeAssociatesLLC的董事长,鲁特格斯大学应用数学统计专业博士,休哈和格兰特奖得主,美国质量协会会员,质量国际学院研究员。



Roger W. Hoerl is manager of GE Global Research’s applied statistics lab. He has a doctorate in applied statistics from the University of Delaware in Newark. Hoerl is an ASQ fellow, a recipient of the ASQ Shewhart Medal and Brumbaugh Award, and an Academician in the International Academy for Quality.
RogerW.Hoerl通用全球应用统计研究实验室管理人,他获得了纽瓦克特拉华大学应用统计专业博士学位,同时他是美国质量协会会员,美国质量协会休哈奖和布鲁堡夫奖的得主,质量国际学院的研究员。
**
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